Give me a break! Unavoidable fatigue effects in cognitive pupillometry
Ikusi/ Ireki
Data
2023Egilea
McLaughlin, Drew J.
Zink, Maggie E.
Gaunt, Lauren
Reilly, Jamie
Sommers, Mitchell S.
Van Engen, Kristin J.
Peelle, Jonathan E.
McLaughlin, D. J., Zink, M. E., Gaunt, L., Reilly, J., Sommers, M. S., Van Engen, K. J., & Peelle, J. E. (2023). Give me a break! Unavoidable fatigue effects in cognitive pupillometry. Psychophysiology, 60, e14256. https://doi.org/10.1111/psyp.14256
Psychophysiology
Psychophysiology
Laburpena
Pupillometry has a rich history in the study of perception and cognition. One
perennial challenge is that the magnitude of the task-evoked
pupil response
diminishes
over the course of an experiment, a phenomenon we refer to as a
fatigue effect. Reducing fatigue effects may improve sensitivity to task effects and
reduce the likelihood of confounds due to systematic physiological changes over
time. In this paper, we investigated the degree to which fatigue effects could be
ameliorated by experimenter intervention. In Experiment 1, we assigned participants
to one of three groups—no
breaks, kinetic breaks (playing with toys, but no
social interaction), or chatting with a research assistant—and
compared the pupil
response across conditions. In Experiment 2, we additionally tested the effect of
researcher observation. Only breaks including social interaction significantly reduced
the fatigue of the pupil response across trials. However, in all conditions
we found robust evidence for fatigue effects: that is, regardless of protocol, the
task-evoked
pupil response was substantially diminished (at least 60%) over the
duration of the experiment. We account for the variance of fatigue effects in our
pupillometry data using multiple common statistical modeling approaches (e.g.,
linear mixed-effects
models of peak, mean, and baseline pupil diameters, as well
as growth curve models of time-course
data). We conclude that pupil attenuation
is a predictable phenomenon that should be accommodated in our experimental
designs and statistical models. Agencia Estatal de Investigación,
Grant/Award Number: CEX2020-001010-
S;
Eusko Jaurlaritza;
National Institutes of Health, Grant/
Award Number: R01 DC014281 and
R01 DC019507; National Science
Foundation, Grant/Award Number:
DGE-1745038